Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	19,478
2	New Jersey	18,246
3	Massachusetts	14,739
4	Rhode Island	14,366
5	District of Columbia	12,775
6	Connecticut	12,086
7	Delaware	9,973
8	Illinois	9,807
9	Maryland	9,199
10	Louisiana	8,871
11	Nebraska	7,685
12	Iowa	6,458
13	Pennsylvania	6,082
14	Michigan	5,906
15	South Dakota	5,835
16	Virginia	5,495
17	Mississippi	5,484
18	Indiana	5,437
19	Colorado	4,696
20	Minnesota	4,589
21	Georgia	4,399
22	New Mexico	3,882
23	Alabama	3,844
24	Tennessee	3,664
25	Kansas	3,528
26	New Hampshire	3,526
27	North Dakota	3,519
28	Wisconsin	3,368
29	Utah	3,293
30	Ohio	3,147
31	Washington	3,086
32	Arizona	3,056
33	California	3,047
34	North Carolina	2,956
35	Nevada	2,905
36	Florida	2,735
37	Arkansas	2,673
38	South Carolina	2,457
39	Texas	2,398
40	Kentucky	2,346
41	Missouri	2,284
42	Maine	1,798
43	Oklahoma	1,719
44	Idaho	1,673
45	Vermont	1,586
46	Wyoming	1,580
47	Puerto Rico	1,259
48	West Virginia	1,158
49	Oregon	1,042
50	Alaska	705
51	Montana	491
52	Hawaii	454

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Massachusetts	223
2	Nebraska	131
3	Maryland	119
4	Tennessee	106
5	Arizona	105
6	District of Columbia	101
7	Illinois	97
8	Mississippi	91
9	Rhode Island	91
10	Arkansas	89
11	Virginia	89
12	Louisiana	87
13	Connecticut	83
14	Iowa	79
15	Utah	75
16	Delaware	73
17	North Carolina	73
18	New Mexico	71
19	Wisconsin	68
20	South Dakota	63
21	Alabama	61
22	California	61
23	Minnesota	60
24	New Jersey	60
25	Indiana	58
26	New York	57
27	Michigan	54
28	Georgia	52
29	Texas	52
30	South Carolina	51
31	Kansas	46
32	North Dakota	44
33	Pennsylvania	43
34	Washington	41
35	Florida	40
36	Kentucky	40
37	Colorado	39
38	Missouri	39
39	Ohio	36
40	New Hampshire	35
41	Nevada	34
42	Idaho	25
43	Oklahoma	25
44	Puerto Rico	25
45	Maine	23
46	Alaska	21
47	Oregon	12
48	West Virginia	12
49	Wyoming	6
50	Vermont	4
51	Montana	3
52	Hawaii	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,537
2	New Jersey	1,337
3	Connecticut	1,118
4	Massachusetts	1,037
5	Rhode Island	700
6	District of Columbia	670
7	Louisiana	617
8	Michigan	558
9	Pennsylvania	448
10	Illinois	447
11	Maryland	436
12	Delaware	385
13	Indiana	327
14	Mississippi	262
15	Colorado	259
16	Georgia	197
17	Ohio	196
18	Minnesota	194
19	New Hampshire	194
20	Iowa	181
21	New Mexico	178
22	Virginia	167
23	Washington	150
24	Nevada	139
25	Arizona	135
26	Alabama	133
27	Missouri	130
28	Florida	119
29	California	111
30	Wisconsin	106
31	Kentucky	102
32	South Carolina	100
33	Nebraska	97
34	North Carolina	92
35	North Dakota	90
36	Vermont	88
37	Oklahoma	86
38	Kansas	77
39	Maine	70
40	South Dakota	70
41	Texas	60
42	Tennessee	56
43	Arkansas	47
44	Idaho	46
45	Puerto Rico	43
46	West Virginia	43
47	Oregon	37
48	Utah	36
49	Wyoming	29
50	Montana	15
51	Hawaii	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Massachusetts	14
2	Rhode Island	7
3	Illinois	6
4	Maryland	6
5	New Jersey	6
6	Louisiana	5
7	Mississippi	5
8	Connecticut	4
9	New Hampshire	4
10	Ohio	4
11	Pennsylvania	4
12	Arizona	3
13	Delaware	3
14	District of Columbia	3
15	Indiana	3
16	Iowa	3
17	New Mexico	3
18	New York	3
19	Colorado	2
20	Georgia	2
21	Michigan	2
22	Minnesota	2
23	North Dakota	2
24	Virginia	2
25	Alabama	1
26	California	1
27	Florida	1
28	Kansas	1
29	Kentucky	1
30	Maine	1
31	Nebraska	1
32	Nevada	1
33	North Carolina	1
34	South Carolina	1
35	Tennessee	1
36	Wisconsin	1
37	Alaska	0
38	Arkansas	0
39	Hawaii	0
40	Idaho	0
41	Missouri	0
42	Montana	0
43	Oklahoma	0
44	Oregon	0
45	Puerto Rico	0
46	South Dakota	0
47	Texas	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	123,538	1	99
Lake	Tennessee	119,014	2	99
Dakota	Nebraska	84,340	3	99
Lincoln	Arkansas	75,553	4	99
Nobles	Minnesota	72,033	5	99
Davidson	Tennessee	8,284	254	91
Richland	South Carolina	3,781	698	77
Pierce	Washington	2,203	1110	64
Orange	California	2,138	1134	63
York	South Carolina	1,516	1449	53

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Early	Georgia	3,140	1	99
Terrell	Georgia	3,048	2	99
Hancock	Georgia	2,956	3	99
Randolph	Georgia	2,803	4	99
Essex	New Jersey	2,111	5	99
Richland	South Carolina	159	620	80
Davidson	Tennessee	94	857	72
Pierce	Washington	91	876	72
Orange	California	50	1209	61
York	South Carolina	36	1373	56

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.